Clinically validated machine learning algorithm for detecting residual diseases with multicolor flow cytometry analysis in acute myeloid leukemia and myelodysplastic syndrome

Ko, Bor-Sheng; Wang, Yufen; Li, Jeng-Lin; Li, Chi‐Cheng; Weng, Pei-Yi; Hsu, Szu-Chun; Hou, Hsin‐An; Huang, Huai-Hsuan; Yao, Ming; Lin, Chun‐Chi; Liu, Jia-Hau; Tsai, Cheng‐Hong; Huang, Tai‐Chung; Wu, Shang‐Ju; Huang, Shang-Yi; Chou, Wen‐Chien; Tien, Hwei‐Fang; Lee, Chi-Chun; Tang, Jih‐Luh

doi:10.1016/j.ebiom.2018.10.042

Cited by 67 publications

(44 citation statements)

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“…This sample(s) trains a classification model that then classifies the cells of similar ungated samples. Examples of models tested in this category include the neural network , support vector machine , and hierarchical agglomerative hierarchy construction . On the other hand, OpenCyto and flowDensity approach the problem by mimicking the manual gating process.…”

Section: Data Handling Evaluation Storage and Repositoriesmentioning

confidence: 99%

Guidelines for the use of flow cytometry and cell sorting in immunological studies (second edition)

et al. 2019

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These guidelines are a consensus work of a considerable number of members of the immunology and flow cytometry community. They provide the theory and key practical aspects of flow cytometry enabling immunologists to avoid the common errors that often undermine immunological data. Notably, there are comprehensive sections of all major immune cell types with helpful Tables detailing phenotypes in murine and human cells. The latest flow cytometry techniques and applications are also described, featuring examples of the data that can be generated and, importantly, how the data can be analysed. Furthermore, there are sections detailing tips, tricks and pitfalls to avoid, all written and peer‐reviewed by leading experts in the field, making this an essential research companion.

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Section: Data Handling Evaluation Storage and Repositoriesmentioning

confidence: 99%

Guidelines for the use of flow cytometry and cell sorting in immunological studies (second edition)

et al. 2019

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“…Internationally, automatic analysis with the aid of artificial intelligence has covered a variety of diseases, ranging from "benign" conditions such as diabetic retinopathy and Alzheimer's disease [7], to malignant tumors such as breast cancer [26][27][28], lung cancer [29], liver cancer [30], skin cancer [31], osteosarcoma [32], and lymphoma [33,34], with an accuracy rate of 89.4-97.8%, and an AUC score of 0.85-0.94 [7,27,31]. In addition, various AI systems related to breast cancer have penetrated through different levels of IDC, such as histology-assisted and cytology-assisted diagnosis, mitotic cell count, lymph node metastasis assessment [9,10,18,22], breast cancer drug development and others [8], with an accuracy rate of 82.7-92.4% and an AUC score of 0.97 [27,28].…”

Section: Discussionmentioning

confidence: 99%

Automated quantitative analysis of Ki-67 staining and HE images recognition and registration based on whole tissue sections in breast carcinoma

et al. 2020

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Background: The scoring of Ki-67 is highly relevant for the diagnosis, classification, prognosis, and treatment in breast invasive ductal carcinoma (IDC). Traditional scoring method of Ki-67 staining followed by manual counting, is time-consumption and inter−/intra observer variability, which may limit its clinical value. Although more and more algorithms and individual platforms have been developed for the assessment of Ki-67 stained images to improve its accuracy level, most of them lack of accurate registration of immunohistochemical (IHC) images and their matched hematoxylin-eosin (HE) images, or did not accurately labelled each positive and negative cell with Ki-67 staining based on whole tissue sections (WTS). In view of this, we introduce an accurate image registration method and an automatic identification and counting software of Ki-67 based on WTS by deep learning. Methods: We marked 1017 breast IDC whole slide imaging (WSI), established a research workflow based on the (i) identification of IDC area, (ii) registration of HE and IHC slides from the same anatomical region, and (iii) counting of positive Ki-67 staining. Results: The accuracy, sensitivity, and specificity levels of identifying breast IDC regions were 89.44, 85.05, and 95.23%, respectively, and the contiguous HE and Ki-67 stained slides perfectly registered. We counted and labelled each cell of 10 Ki-67 slides as standard for testing on WTS, the accuracy by automatic calculation of Ki-67 positive rate in attained IDC was 90.2%. In the human-machine competition of Ki-67 scoring, the average time of 1 slide was 2.3 min with 1 GPU by using this software, and the accuracy was 99.4%, which was over 90% of the results provided by participating doctors.

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“…When combined with class labels y i , this table can then be used to train and evaluate a multivariate classification model using any standard classification method. This is a typical approach (e.g., Aghaeepour et al ; Ko et al ) for building diagnostic classifiers, consisting of two steps: first, P features are defined, and second, a classification model is constructed given vectors of the P ‐dimensional feature values for each subject.…”

Section: Methodsmentioning

confidence: 99%

“…In this general context, in order to reduce human bias in the analysis process and improve reproducibility of the analysis results, a variety of different computational approaches have been proposed and developed in prior work to process, analyze, and classify cytometry samples (Pyne et al ; Qian et al ; Aghaeepour et al ; Finak et al ; Hassan et al ; Saeys et al ; Johnsson et al ; Lee et al ; Arvaniti and Claassen ; Li et al ; Lun et al ; Rahim et al ; Ko et al ; Hu et al ; Lux ; Lee et al ). For identification of cell populations, auto‐gating approaches include unsupervised, supervised, semi‐supervised (Lee et al ), and constrained unsupervised (Lee et al ) methods.…”

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confidence: 99%

Machine Learning of Discriminative Gate Locations for Clinical Diagnosis

Putzel

et al. 2019

Cytometry Pt A

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High‐throughput single‐cell cytometry technologies have significantly improved our understanding of cellular phenotypes to support translational research and the clinical diagnosis of hematological and immunological diseases. However, subjective and ad hoc manual gating analysis does not adequately handle the increasing volume and heterogeneity of cytometry data for optimal diagnosis. Prior work has shown that machine learning can be applied to classify cytometry samples effectively. However, many of the machine learning classification results are either difficult to interpret without using characteristics of cell populations to make the classification, or suboptimal due to the use of inaccurate cell population characteristics derived from gating boundaries. To date, little has been done to optimize both the gating boundaries and the diagnostic accuracy simultaneously. In this work, we describe a fully discriminative machine learning approach that can simultaneously learn feature representations (e.g., combinations of coordinates of gating boundaries) and classifier parameters for optimizing clinical diagnosis from cytometry measurements. The approach starts from an initial gating position and then refines the position of the gating boundaries by gradient descent until a set of globally‐optimized gates across different samples are achieved. The learning procedure is constrained by regularization terms encoding domain knowledge that encourage the algorithm to seek interpretable results. We evaluate the proposed approach using both simulated and real data, producing classification results on par with those generated via human expertise, in terms of both the positions of the gating boundaries and the diagnostic accuracy. © 2019 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.

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Clinically validated machine learning algorithm for detecting residual diseases with multicolor flow cytometry analysis in acute myeloid leukemia and myelodysplastic syndrome

Cited by 67 publications

References 44 publications

Guidelines for the use of flow cytometry and cell sorting in immunological studies (second edition)

Guidelines for the use of flow cytometry and cell sorting in immunological studies (second edition)

Automated quantitative analysis of Ki-67 staining and HE images recognition and registration based on whole tissue sections in breast carcinoma

Machine Learning of Discriminative Gate Locations for Clinical Diagnosis

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